Image warping is fundamental for a variety of multimedia applications such as image resizing (e.g, [1]}), texture mapping (e.g., [2]}), image-based rendering (e.g., [3]}), panorama stitching (e.g., [4]}), stereo reconstruction (e.g., [5]}), optical flow computation (e.g., [6]}), to name but a few. Image warping can be briefly described as transforming a source image \(I_{1}\)  into a target image \(I_{2}\)  under a geometric mapping \(\mathbf {H}\) , which can be linear, affine, perspective, non-parametric etc.[7]}. Particularly, assuming \(I_{2}\)  has \(M\)  rows and \(N\)  columns, each pixel \((i,j)\)  in the target image lattice \([1 \quad M]\times [1 \quad N]\)  has a correspondence image point \([x,y]=\mathbf {H}^{-1}([i,j])\)  in the source image, from which the intensity value \(I_{1}[x,y]\)  is assigned to \(I_{2}[i,j]\) . The central problem of image warping is to estimate \(I_{1}[x,y]\)  from pixels in the vicinity of \([x,y]\) , which are generally not on the integer grid, and hence the true intensity \(I_{1}[x,y]\)  is not immediately available.
